Viewing the global health system as a complex adaptive system - implications for research and practice

Abstract

The global health system (GHS) is ill-equipped to deal with the increasing number of transnational challenges. The GHS needs reform to enhance global resilience to future risks to health. In this article we argue that the starting point for any reform must be conceptualizing and studying the GHS as a complex adaptive system (CAS) with a large and escalating number of interconnected global health actors that learn and adapt their behaviours in response to each other and changes in their environment. The GHS can be viewed as a multi-scalar, nested health system comprising all national health systems together with the global health architecture, in which behaviours are influenced by cross-scale interactions. However, current methods cannot adequately capture the dynamism or complexity of the GHS or quantify the effects of challenges or potential reform options. We provide an overview of a selection of systems thinking and complexity science methods available to researchers and highlight the numerous policy insights their application could yield. We also discuss the challenges for researchers of applying these methods and for policy makers of digesting and acting upon them. We encourage application of a CAS approach to GHS research and policy making to help bolster resilience to future risks that transcend national boundaries and system scales.

Keywords: complex adaptive system; global health system; health system.

Figure 1.. An illustrative simplified multi-level systems view of the COVID-19 vaccination subsystem.

Note: System behaviour responds to bottom up and top-down influences, with higher level entities (global actors (pharmaceutical companies) and initiatives (COVAX)) influencing COVID-19 vaccine production, affordability and deployment options of lower-level agents; and actions of lower level agents (e.g. patients uptake or not of vaccines) influencing global responses.

Figure 2.. Simplified illustration of a reinforcing and balancing loop in relation to HICs entering into bilateral deals to secure COVID-19 vaccines.

Note to Figure:

1. 1.

   Principles of balancing and reinforcing feedback loops: “+” indicates a change in the same direction, “-”in the opposite direction to the change in the variable at the start of an arrow. A reinforcing (R) loop has an even number, a balancing (B) loop an uneven number of “-”relationships. “//” indicates a delay.

2. 2.

   The principle context of COVAX has been omitted for simplicity reasons, and only the impact of making bilateral agreements has been illustrated.

Figure 3.. Illustration of a network structure under two scenarios, showing how the failure of a single node may lead to very different systemic outcomes depending on its position in the network.

The network on the right remains intact whereas the network on the left disintegrates into four fragments.